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Improving the Precision and Speed of Euler Angles Computation from Low-Cost Rotation Sensor Data

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Improving the Precision and Speed of Euler Angles Computation from Low-Cost Rotation Sensor Data Sensors 2015, 15, 7016-7039; doi:10.3390/s150307016 OPEN ACCESS sensors ISSN 1424-8220 www.mdpi.com/journal/sensors Article Improving the Precision and Speed of Euler Angles Computation from Low-Cost Rotation Sensor Data Aleš Janota *, Vojtech Šimák, Dušan Nemec and Jozef Hrbček Department of Control & Information Systems, Faculty of Electrical Engineering, University of Žilina, Univerzitná 8215/1, Žilina 010 26, Slovakia; E-Mail: [email protected] (V.Š.); [email protected] (D.N.); [email protected] (J.H.) * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +421-41-513-3356; Fax: +421-41-513-1515. Academic Editor: Stefano Mariani Received: 5 January 2015 / Accepted: 17 March 2015 / Published: 23 March 2015 Abstract: This article compares three different algorithms used to compute Euler angles from data obtained by the angular rate sensor (e.g., MEMS gyroscope)—the algorithms based on a rotational matrix, on transforming angular velocity to time derivations of the Euler angles and on unit quaternion expressing rotation. Algorithms are compared by their computational efficiency and accuracy of Euler angles estimation. If attitude of the object is computed only from data obtained by the gyroscope, the quaternion-based algorithm seems to be most suitable (having similar accuracy as the matrix-based algorithm, but taking approx. 30% less clock cycles on the 8-bit microcomputer). Integration of the Euler angles’ time derivations has a singularity, therefore is not accurate at full range of object’s attitude. Since the error in every real gyroscope system tends to increase with time due to its offset and thermal drift, we also propose some measures based on compensation by additional sensors (a magnetic compass and accelerometer). Vector data of mentioned secondary sensors has to be transformed into the inertial frame of reference. While transformation of the vector by the matrix is slightly faster than doing the same by quaternion, the compensated sensor system utilizing a matrix-based algorithm can be approximately 10% faster than the system utilizing quaternions (depending on implementation and hardware). Keywords: gyroscope; Euler angle; inertial navigation; MEMS; rotational matrix Sensors 2015, 15 7017 1. Introduction Micro-Electro-Mechanical systems (MEMS) represent the integration of mechanical elements, sensors, actuators, and electronics on a common silicon substrate through the utilization of microfabrication technology [1]. The number of MEMS used in various applications is permanently growing due to the small dimensions, light weight, lower power consumption, higher reliability, and relatively low cost which makes them commercially available. Typical MEMS-based low-cost products are accelerometers, gyroscopes, pressure sensors, microphones, digital mirror displays, micro pumps, etc. For the purpose of low-cost navigation solutions MEMS-based inertial sensors (accelerometers and gyroscopes) have been developed since orientation of an object in the three-dimensional space is key information needed for navigation, guidance and control tasks. MEMS inertial sensors may be found in variety of applications from traditional ones (navigation and positioning of various transport means and/or robots) to sensing of human body walking and movement [2–5], daily life surveillance [6] or new commercial applications available through smart phones [7]. Most studies on MEMS gyroscopes are focused on their performance, and common methods to improve the performance [8]. Unlike non-micro devices MEMS sensors experience more errors that build up over time, corrupting the precision of the measurements and eventually rendering the navigation solution useless [9,10]. Thus the first and easiest-to-measure performance criterion of a gyro is its static readout as a function of time. Accuracy is usually limited by electrical noise, systematic errors and/or mechanical thermal noise [11,12]. The static compensation of sensor inaccuracies can be enabled by proper calibration methods designed for MEMS gyroscopes and accelerometers [13]. The principle of recently developed micro-machine gyroscopes, their structures and classification can be found in [14]. Generally, gyroscopes measure rotational rate, which can be integrated to yield changes in orientation. An effective method most used to parametrize the orientation space is based on usage of so called Euler angles. Euler angles are used as a framework for formulating and solving the equations for conservation of angular momentum. This article has been written with motivation to analyze and show how precision and speed of computations of Euler angles could be improved when processing data from the MEMS gyroscope. It is organized as follows: Section 1 (Introduction) describes theoretically several methods of notation to express rotation of a body (particularly the rotation matrix, Euler angles, rotation around arbitrary axis, and quaternion). Section 2 (Experimental Section) is focused on comparison of errors occurring when algorithms utilizing described notations process data from the gyroscope. If applicable, more versions of the same algorithm are considered (focused either on accuracy or fastness of computation). At the end of the section there is discussion on how errors presented in real gyroscopes could be compensated. Section 3 (Results and Discussion) summarizes analyzed properties and gives final comparison and overview of obtained results. Finally, Section 4 gives the conclusions. The article is an extended version of the conference paper [15], elaborated and supported by the VEGA1/0453/12 grant and used with kind permission of Springer Science + Business Media. Article extensions resulted from the work under another project as stated in the Acknowledgments section. The purpose of the inertial navigation in the 3D space is to determine six independent variables: translation of an object in three axes and its rotation in three axes, relative to the inertial frame of the Sensors 2015, 15 7018 reference body. In this article we describe possible ways how to express rotation (attitude) of the object and calculate it from angular velocity measured by the gyroscope. We consider the Cartesian (orthonormal) right-hand coordinate system oriented by convention NED, i.e., North-East-Down. Moving object axes’ orientations are x → forward, y → right and z → down (Figure 1). Two reference frames are used: • Frame of reference joined with Earth (considered to be approximately inertial), marked S. All variables measured with respect to Earth will be marked without a dash. • Frame of reference joined with rotating object, marked S’. All variables measured onboard the moving object will be marked with a dash. Figure 1. Orientation of the coordinate system axes. First we will analyze four used methods of notation that allow us to express rotation of a body. Differences among those individual approaches can be seen in data redundancy and consumption of computer time during processing of raw data from the gyroscope and during conversion from one notation to another (which has direct impact on algorithm efficiency). 1.1. Euler Angles Euler angles are expressing rotation of the object as a sequence of three rotations around objects’ local coordinate axes. This way of rotation expression is most interpretative and has zero data redundancy because only three real numbers are needed. Different sequence of axis rotation produces different resultant rotation; therefore Euler angles are defined according to chosen sequence (convention). In aviation the most used convention is z-y-x convention (sometimes called Yaw-Pitch-Roll convention or 3-2-1, see Figure 2): 1. Rotate the object around its z-axis by angle Yaw (marked γ); 2. Rotate the object around its new y1-axis by angle Pitch (marked β); 3. Rotate the object around its new x2-axis by angle Roll (marked α). Rotation order of z-y-x convention can be expressed by the following operator: ℜ = ℜ x2 (ℜ y1 ()ℜ z ) α ,β ,γ α β γ (1) Inverse rotation is given by the reversed rotation order by inverted angles: − ′ ℜ 1 = ℜ z1 (ℜ y2 (ℜ x )) α ,β ,γ −γ −β −α (2) Sensors 2015, 15 7019 Main disadvantage of representing object’s rotation by Euler angles is a lack of the simple algorithm for vector transformation. This can be realized by transferring Euler angles to the rotation matrix by Equation (10) and following application of Equation (4). Trivial chaining (adding) of two rotations represented by Euler angles is not possible. 1 2 3 Figure 2. Euler angles for 3-2-1 convention. 1.2. The Rotational Matrix The rotation matrix defines change of coordinates of the object in the coordinate system S during rotational movement. It is a typical representation of object’s attitude (very often used, e.g., in computer graphics). It is clear that this form has the greatest data redundancy due to needs of saving nine real numbers: R11 R12 R13 R = R R R 21 22 23 (3) R31 R32 R33 Transformation of coordinates from the system S to the system S' can be done by multiplication of the position column vector r by the rotation matrix: r′ = R ⋅ r (4) Result of rotation R1 followed by R2 is given by matrix multiplication: = ⋅ R R2 R1 (5) Inverse rotation is given by the transposed matrix: R −1 = RT (6) While the original vector r has the same length as the resultant vector r', the rotational matrix has to be orthogonal with its determinant equal to 1. The matrix is orthogonal when all its row or column vectors are perpendicular to each other. The following algorithm can be used to normalize the matrix to be pure rotational [16]: Sensors 2015, 15 7020 1. Calculate deviations eik from orthogonality of the matrix columns: = ⋅ R11 R12 R13 exy X Y X = R Y = R Z = R e = Y ⋅ Z 21 22 23 yz (7) = ⋅ R31 R32 R33 ezx Z X 2. Distribute errors among all columns: e e X = X − xy Y − zx Z ort 2 2 exy eyz Y = Y − X − Z (8) ort 2 2 e e Z = Z − zx X − yz Y ort 2 2 3.
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